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[dygraph hybrid pp for interleave] Save/Load for interleaved pipeline. (
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...fluid/tests/unittests/collective/fleet/hybrid_parallel_pp_save_load_with_virtual_stage.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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from __future__ import division | ||
from __future__ import print_function | ||
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import unittest | ||
import paddle | ||
import numpy as np | ||
import random | ||
import os | ||
import shutil | ||
import tempfile | ||
import paddle.distributed as dist | ||
import paddle.distributed.fleet as fleet | ||
from hybrid_parallel_pp_transformer_with_virtual_stage import ModelPipe, set_random_seed | ||
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batch_size = 8 | ||
length = 8 | ||
micro_batch_size = 2 | ||
vocab_size = 128 | ||
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class TestDistPPSaveLoadTraning(unittest.TestCase): | ||
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def setUp(self): | ||
strategy = fleet.DistributedStrategy() | ||
self.model_parallel_size = 1 | ||
self.data_parallel_size = 1 | ||
self.pipeline_parallel_size = 2 | ||
strategy.hybrid_configs = { | ||
"dp_degree": self.data_parallel_size, | ||
"mp_degree": self.model_parallel_size, | ||
"pp_degree": self.pipeline_parallel_size, | ||
} | ||
strategy.pipeline_configs = { | ||
"accumulate_steps": batch_size // micro_batch_size, | ||
"micro_batch_size": micro_batch_size | ||
} | ||
fleet.init(is_collective=True, strategy=strategy) | ||
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def test_pp_model(self): | ||
hcg = fleet.get_hybrid_communicate_group() | ||
word_size = hcg.get_model_parallel_world_size() | ||
dp_id = hcg.get_data_parallel_rank() | ||
pp_id = hcg.get_stage_id() | ||
rank_id = dist.get_rank() | ||
topology = hcg.topology() | ||
set_random_seed(1024, dp_id, rank_id) | ||
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model = ModelPipe(topology) | ||
scheduler = paddle.optimizer.lr.PiecewiseDecay(boundaries=[2], | ||
values=[0.001, 0.002], | ||
verbose=True) | ||
optimizer = paddle.optimizer.SGD(learning_rate=scheduler, | ||
parameters=model.parameters()) | ||
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model = fleet.distributed_model(model) | ||
optimizer = fleet.distributed_optimizer(optimizer) | ||
output_dir = tempfile.mkdtemp() | ||
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# warmup step | ||
for step_id in range(2): | ||
x_data = np.random.randint(0, vocab_size, size=[batch_size, length]) | ||
x = paddle.to_tensor(x_data) | ||
x.stop_gradient = True | ||
loss = model.train_batch([x, x], optimizer, scheduler) | ||
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model._layers.save_state_dict(output_dir) | ||
paddle.save(optimizer.state_dict(), | ||
os.path.join(output_dir, "model_state.pdopt")) | ||
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# construct data | ||
test_steps = 5 | ||
np_data = np.random.randint(0, | ||
vocab_size, | ||
size=[test_steps, batch_size, length]) | ||
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origin_loss = [] | ||
for step_id in range(5): | ||
x_data = np_data[step_id, :] | ||
x = paddle.to_tensor(x_data) | ||
x.stop_gradient = True | ||
loss = model.train_batch([x, x], optimizer, scheduler) | ||
origin_loss.append(loss.numpy()) | ||
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# test step | ||
model._layers.set_state_dir(output_dir) | ||
opt_dict = paddle.load(os.path.join(output_dir, "model_state.pdopt")) | ||
optimizer.set_state_dict(opt_dict) | ||
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for step_id in range(5): | ||
x_data = np_data[step_id, :] | ||
x = paddle.to_tensor(x_data) | ||
x.stop_gradient = True | ||
loss = model.train_batch([x, x], optimizer, scheduler) | ||
print("origin loss: ", origin_loss[step_id], "current loss: ", | ||
loss.numpy()) | ||
np.testing.assert_allclose(loss.numpy(), origin_loss[step_id]) | ||
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# finally, remove the model/optimizer path | ||
shutil.rmtree(output_dir) | ||
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if __name__ == "__main__": | ||
unittest.main() |
2 changes: 1 addition & 1 deletion
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python/paddle/fluid/tests/unittests/collective/fleet/hybrid_parallel_pp_transformer.py
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